ReviewDer Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete2021
[Artificial intelligence in ex vivo confocal laser scanning microscopy].
Review in Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- S1 guideline for imaging diagnostics for skin diseases.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2025Guideline
- [Ex vivo confocal laser scanning microscopy in dermatology : Techniques, indications, and perspectives].Dermatologie (Heidelberg, Germany) · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVisual data, such as clinical photographs or pictures from imaging examination methods, such as ex vivo confocal laser scanning microscopy (CLSM), are particularly suitable for machine learning techniques.
objectivesThe aim was to find out whether data have already been published on this innovative application in ex vivo CLSM and what potential challenges and limitations could arise. MATERIAL AND
methodsReview of the literature and summary of current knowledge and personal experience on the use of artificial intelligence (AI) in ex vivo CLSM.
resultsSuccessful integration of digital hematoxylin-eosin-like staining has made ex vivo CLSM significantly more accessible for digital assessments. Several machine learning techniques have been developed to date in such a way that they have been able to identify malignant skin lesions on clinical photographs and pathological microscopic images with similar accuracy compared to experts, or even find visual patterns that have been overlooked by experts and that correlate with certain dermatological diseases. One study on the use of AI in ex vivo CLSM for automated tumor detection has been published to date. Several challenges and limitations can arise when using AI in ex vivo CLSM.
conclusionsThe already digitized ex vivo CLSM, which was established for rapid section examination purposes, is a predestined method for the development and use of new applications with machine learning in the healthcare sector. The results of further studies on this topic are anticipated with great hope.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.